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Related Concept Videos

Poisson Probability Distribution01:09

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...

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Related Experiment Video

Updated: May 7, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Sample size calculation for differential expression analysis of RNA-seq data under Poisson distribution.

Chung-I Li1, Pei-Fang Su, Yan Guo

  • 1Department of Applied Mathematics, National Chiayi University, No. 300, Xuefu Rd., East Dist., Chiayi City, Taiwan.

International Journal of Computational Biology and Drug Design
|October 4, 2013
PubMed
Summary

This study introduces new sample size calculation methods for RNA sequencing (RNA-seq) differential expression studies. These methods are efficient, account for multiple testing, and are validated with simulations and real data.

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Area of Science:

  • Biostatistics
  • Bioinformatics
  • Genomics

Background:

  • Sample size determination is crucial for robust biomedical research.
  • Existing methods for RNA sequencing (RNA-seq) differential expression analysis are limited.
  • A widely applicable sample size method for RNA-seq is needed.

Purpose of the Study:

  • To propose novel sample size calculation methods for RNA-seq differential expression studies.
  • To extend these methods for single-gene and multiple-gene analyses.
  • To address the multiple testing problem by controlling the false discovery rate.

Main Methods:

  • Development of sample size calculation methods based on the Poisson distribution for RNA-seq data.
  • Extension to multiple-gene analysis with false discovery rate control.
  • Derivation of closed-form sample size formulas requiring minimum fold change and read count.

Main Results:

  • Proposed methods provide closed-form sample size formulas, reducing computational intensity.
  • Simulation studies demonstrate the effectiveness and desired power of the new methods.
  • The methods were successfully applied to three real-world RNA-seq datasets.

Conclusions:

  • The developed sample size calculation methods are applicable and effective for RNA-seq differential expression studies.
  • These methods offer a practical solution for experimental design in genomics research.
  • The approach facilitates more reliable and powerful RNA-seq study designs.